Walsh, PE
(2017)
Variable selection for classification in complex ophthalmic data : a multivariate statistical framework.
PhD thesis, University of Liverpool.
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Abstract
Variable selection is an essential part of the process of model-building for classification or prediction. Some of the challenges of variable selection are heterogeneous variance-covariance matrices, differing scales of variables, non-normally distributed data and missing data. Statistical methods exist for variable selection however these are often univariate, make restrictive assumptions about the distribution of data or are expensive in terms of the computational power required. In this thesis I focus on filter methods of variable selection that are computationally fast and propose a metric of discrimination. The main objectives of this thesis are (1) to propose a novel Signal-to-Noise Ratio (SNR) discrimination metric accommodating heterogeneous variance-covariance matrices, (2) to develop a multiple forward selection (MFS) algorithm employing the novel SNR metric, (3) to assess the performance of the MFS-SNR algorithm compared to alternative methods of variable selection, (4) to investigate the ability of the MFS-SNR algorithm to carry out variable selection when data are not normally distributed and (5) to apply the MFS-SNR algorithm to the task of variable selection from real datasets. The MFS-SNR algorithm ... (continues)
| Item Type: | Thesis (PhD) |
|---|---|
| Divisions: | Faculty of Health and Life Sciences > Faculty of Health and Life Sciences |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 23 Aug 2018 13:45 |
| Last Modified: | 05 Nov 2024 20:02 |
| DOI: | 10.17638/03019718 |
| Supervisors: |
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| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3019718 |
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